This paper develops a novel COllaborative-Online-Learning (COOL)-enabled motion control framework for multi-robot systems to avoid collision amid randomly moving obstacles whose motion distributions are partially observable through decentralized data streams. To address the notable challenge of data acquisition due to occlusion, a COOL approach based on the Dirichlet process mixture model is proposed to efficiently extract motion distribution information by exchanging among robots selected learning structures. By leveraging the fine-grained local-moment information learned through COOL, a data-stream-driven ambiguity set for obstacle motion is constructed. We then introduce a novel ambiguity set propagation method, which theoretically admits the derivation of the ambiguity sets for obstacle positions over the entire prediction horizon by utilizing obstacle current positions and the ambiguity set for obstacle motion. Additionally, we develop a compression scheme with its safety guarantee to automatically adjust the complexity and granularity of the ambiguity set by aggregating basic ambiguity sets that are close in a measure space, thereby striking an attractive trade-off between control performance and computation time. Then the probabilistic collision-free trajectories are generated through distributionally robust optimization problems. The distributionally robust obstacle avoidance constraints based on the compressed ambiguity set are equivalently reformulated by deriving separating hyperplanes through tractable semi-definite programming. Finally, we establish the probabilistic collision avoidance guarantee and the long-term tracking performance guarantee for the proposed framework. The numerical simulations are used to demonstrate the efficacy and superiority of the proposed approach compared with state-of-the-art methods.

翻译：本文提出了一种新颖的协作式在线学习赋能运动控制框架，用于多机器人系统在随机移动障碍物环境中实现避碰，其中障碍物的运动分布通过分散式数据流进行部分观测。为应对因遮挡导致的数据获取难题，本文提出了一种基于狄利克雷过程混合模型的协作式在线学习方法，通过在被选机器人间交换学习结构，高效提取障碍物的运动分布信息。利用通过协作式在线学习获得的细粒度局部矩信息，构建了数据流驱动的障碍物运动模糊集。随后，我们引入了一种新颖的模糊集传播方法，该方法理论上允许利用障碍物当前位置及其运动模糊集，推导出整个预测时域内障碍物位置的模糊集。此外，我们开发了一种具备安全性保证的压缩方案，通过聚合测度空间中相近的基本模糊集，自动调整模糊集的复杂度和粒度，从而在控制性能与计算时间之间实现理想的权衡。随后，通过分布鲁棒优化问题生成概率无碰撞轨迹。基于压缩模糊集的分布鲁棒避障约束，通过可处理的半定规划推导分离超平面，实现了等价重构。最后，我们为所提框架建立了概率避碰保证与长期跟踪性能保证。数值仿真验证了所提方法相较于现有先进方法的有效性和优越性。